Hi Rui,
I am sorry for asking you several questions.
In the given example, randomizations (reshuffle) were done 1000 times, and
its 1000 proportion values (results) are stored and it can be seen using
b$t; but I was wondering how the table was randomized (which rows have been
missed/or repeated in each randomizing procedure?).
Is there any way we can see the randomized table and its associated
results? Here in this example, I randomized (or bootstrapped) the table
into three times (R=3) so I would like to store these three tables and look
at them later to know which rows were repeated/missed. Is there any
possibility?
The example data and the code is given below.
Thank you for your help.
####
library(boot)
dat<-structure(list(Sample = structure(c(1L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L), .Label = c("id1",
"id10", "id11", "id12", "id13", "id14", "id15", "id16", "id17",
"id18", "id19", "Id2", "id3", "id4", "id5", "id6", "id7", "id8",
"id9"), class = "factor"), Time1 = c(0L, 1L, 1L, 1L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L), Time2 = c(1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
1L, 1L)), .Names = c("Sample", "Time1", "Time2"), class = "data.frame",
row.names = c(NA,
-19L))
daT<-data.frame(dat %>%
mutate(Time1.but.not.in.Time2 = case_when(
Time1 %in% "1" & Time2 %in% "0" ~ "1"),
Time2.but.not.in.Time1 = case_when(
Time1 %in% "0" & Time2 %in% "1" ~ "1"),
BothTimes = case_when(
Time1 %in% "1" & Time2 %in% "1" ~ "1")))
cols.num <- c("Time1.but.not.in.Time2","Time2.but.not.in.Time1",
"BothTimes")
daT[cols.num] <- sapply(daT[cols.num],as.numeric)
summary(daT)
bootprop <- function(data, index){
d <- data[index, ]
sum(d[["BothTimes"]], na.rm = TRUE)/sum(d[["Time1"]], na.rm = TRUE)
}
R <- 3
set.seed(2020)
b <- boot(daT, bootprop, R)
b
b$t0 # original
b$t
sd(b$t) # bootstrapped estimate of the SE of the sample prop.
hist(b$t, freq = FALSE)
str(b)
b$data
b$seed
b$sim
b$strata
################
On Sat, Jan 23, 2021 at 12:36 AM Marna Wagley <marna.wag...@gmail.com>
wrote:
> Yes Rui, I can see we don't need to divide by square root of sample size.
> The example is great to understand it.
> Thank you.
> Marna
>
>
> On Sat, Jan 23, 2021 at 12:28 AM Rui Barradas <ruipbarra...@sapo.pt>
> wrote:
>
>> Hello,
>>
>> Inline.
>>
>> Às 07:47 de 23/01/21, Marna Wagley escreveu:
>> > Dear Rui,
>> > I was wondering whether we have to square root of SD to find SE, right?
>>
>> No, we don't. var already divides by n, don't divide again.
>> This is the code, that can be seen by running the function name at a
>> command line.
>>
>>
>> sd
>> #function (x, na.rm = FALSE)
>> #sqrt(var(if (is.vector(x) || is.factor(x)) x else as.double(x),
>> # na.rm = na.rm))
>> #<bytecode: 0x55f3ce900848>
>> #<environment: namespace:stats>
>>
>>
>>
>> >
>> > bootprop <- function(data, index){
>> > d <- data[index, ]
>> > sum(d[["BothTimes"]], na.rm = TRUE)/sum(d[["Time1"]], na.rm = TRUE)
>> > }
>> >
>> > R <- 1e3
>> > set.seed(2020)
>> > b <- boot(daT, bootprop, R)
>> > b
>> > b$t0 # original
>> > sd(b$t) # bootstrapped estimate of the SE of the sample prop.
>> > sd(b$t)/sqrt(1000)
>> > pandit*(1-pandit)
>> >
>> > hist(b$t, freq = FALSE)
>>
>>
>> Try plotting the normal densities for both cases, the red line is
>> clearly wrong.
>>
>>
>> f <- function(x, xbar, s){
>> dnorm(x, mean = xbar, sd = s)
>> }
>>
>> hist(b$t, freq = FALSE)
>> curve(f(x, xbar = b$t0, s = sd(b$t)), from = 0, to = 1, col = "blue",
>> add = TRUE)
>> curve(f(x, xbar = b$t0, s = sd(b$t)/sqrt(R)), from = 0, to = 1, col =
>> "red", add = TRUE)
>>
>>
>> Hope this helps,
>>
>> Rui Barradas
>>
>> >
>> >
>> >
>> >
>> > On Fri, Jan 22, 2021 at 3:07 PM Rui Barradas <ruipbarra...@sapo.pt
>> > <mailto:ruipbarra...@sapo.pt>> wrote:
>> >
>> > Hello,
>> >
>> > Something like this, using base package boot?
>> >
>> >
>> > library(boot)
>> >
>> > bootprop <- function(data, index){
>> > d <- data[index, ]
>> > sum(d[["BothTimes"]], na.rm = TRUE)/sum(d[["Time1"]], na.rm =
>> TRUE)
>> > }
>> >
>> > R <- 1e3
>> > set.seed(2020)
>> > b <- boot(daT, bootprop, R)
>> > b
>> > b$t0 # original
>> > sd(b$t) # bootstrapped estimate of the SE of the sample prop.
>> > hist(b$t, freq = FALSE)
>> >
>> >
>> > Hope this helps,
>> >
>> > Rui Barradas
>> >
>> > Às 21:57 de 22/01/21, Marna Wagley escreveu:
>> > > Hi All,
>> > > I was trying to estimate standard error (SE) for the proportion
>> > value using
>> > > some kind of randomization process (bootstrapping or jackknifing)
>> > in R, but
>> > > I could not figure it out.
>> > >
>> > > Is there any way to generate SE for the proportion?
>> > >
>> > > The example of the data and the code I am using is attached for
>> your
>> > > reference. I would like to generate the value of proportion with
>> > a SE using
>> > > a 1000 times randomization.
>> > >
>> > > dat<-structure(list(Sample = structure(c(1L, 12L, 13L, 14L, 15L,
>> 16L,
>> > > 17L, 18L, 19L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L), .Label
>> > = c("id1",
>> > > "id10", "id11", "id12", "id13", "id14", "id15", "id16", "id17",
>> > > "id18", "id19", "Id2", "id3", "id4", "id5", "id6", "id7", "id8",
>> > > "id9"), class = "factor"), Time1 = c(0L, 1L, 1L, 1L, 0L, 0L,
>> > > 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L), Time2 =
>> c(1L,
>> > > 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
>> > > 1L, 1L)), .Names = c("Sample", "Time1", "Time2"), class =
>> > "data.frame",
>> > > row.names = c(NA,
>> > > -19L))
>> > > daT<-data.frame(dat %>%
>> > > mutate(Time1.but.not.in.Time2 = case_when(
>> > > Time1 %in% "1" & Time2 %in% "0" ~ "1"),
>> > > Time2.but.not.in.Time1 = case_when(
>> > > Time1 %in% "0" & Time2 %in% "1" ~ "1"),
>> > > BothTimes = case_when(
>> > > Time1 %in% "1" & Time2 %in% "1" ~ "1")))
>> > > daT
>> > > summary(daT)
>> > >
>> > > cols.num <- c("Time1.but.not.in.Time2","Time2.but.not.in.Time1",
>> > > "BothTimes")
>> > > daT[cols.num] <- sapply(daT[cols.num],as.numeric)
>> > > summary(daT)
>> > > ProportionValue<-sum(daT$BothTimes, na.rm=T)/sum(daT$Time1,
>> na.rm=T)
>> > > ProportionValue
>> > > standard error??
>> > >
>> > > [[alternative HTML version deleted]]
>> > >
>> > > ______________________________________________
>> > > R-help@r-project.org <mailto:R-help@r-project.org> mailing list
>> > -- To UNSUBSCRIBE and more, see
>> > > https://stat.ethz.ch/mailman/listinfo/r-help
>> > > PLEASE do read the posting guide
>> > http://www.R-project.org/posting-guide.html
>> > > and provide commented, minimal, self-contained, reproducible
>> code.
>> > >
>> >
>>
>
[[alternative HTML version deleted]]
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